Consider the following code for illustration propose:
import mod
f1s = ["A1", "B1", "C1"]
f2s = ["A2", "B2", "C2"]
for f1, f2 in zip(f1s,f2s):
# Creating an object
acumulator = mod.AcumulatorObject()
# Using object
acumulator.append(f1)
acumulator.append(f2)
# Output of object
acumulator.print()
So, I use an instance of a class at the beginning of the for to perform an operation. For each tuple in the for I need to perform the same action, however I can not use the same object because it would add the effect of the last iteration. Therefore, at the beginning of every iteration I create a new instance.
My question is if by doing this a memory leak is created? What action I have to do for each object created? (Delete it maybe? Or by assign the new object to the same name it is cleared?)
tl,dr; no
The reference implementation of Python uses reference counting for garbage collection. There are other implementations that use different GC strategies and this affects the precise time at which __del__ methods are called, which may or may not be reliable or timely in PyPy, Jython or IronPython. These differences are not important unless when you are dealing with resources like file pointers and other expensive system resources.
In cPython the GC will wipe out objects when the referencing count is zero. For example, when you do acumulator = mod.AcumulatorObject() inside a for loop, a new object replaces the old one at the next iteration - and since there are no other variables referencing the old object it will be garbage collected in the next GC pass. The reference implementation cPython will spoil you with things like releasing resources automatically when they go out of scope but YMMV regarding other implementations.
That is why many people commented memory leaks are not of concern in Python.
You have complete control over cPython's garbage collector using the cg module. The default settings are pretty conservative and in 10 years doing Python for a living I never had to fire a GC cycle manually - but I've seen a situation where delaying it helped performance:
Yes, I had previously played with sys.setcheckinterval. I changed it to 1000 (from its default of 100), but it didn't do any measurable difference. Disabling Garbage Collection has helped - thanks. This has been the biggest speedup so far - saving about 20% (171 minutes for the whole run, down to 135 minutes) - I'm not sure what the error bars are on that, but it must be a statistically significant increase.
Just follow best practices like wrapping system resources using with or (try/finally blocks) and you should have no problems.
Related
I am running some memory-heavy scripts which iterate over documents in a database, and due to memory constraints on the server I manually delete references to the large object at the conclusion of each iteration:
for document in database:
initial_function_calls()
big_object = memory_heavy_operation(document)
save_to_file(big_object)
del big_object
additional_function_calls()
The initial_function_calls() and additional_function_calls() are each slightly memory-heavy. Do I see any benefit by explicitly deleting the reference to the large object for garbage collection? Alternatively, does leaving it and having it point to a new object in the next iteration suffice?
As often in these cases; it depends. :-/
I'm assuming we're talking about CPython here.
Using del or re-assigning a name reduces the reference count for an object. Only if that reference could reaches 0 can it be de-allocated. So if you inadvertently stashed a reference to big_object away somewhere, using del won't help.
When garbage collection is triggered depends on the amount of allocations and de-allocations. See the documentation for gc.set_threshold().
If you're pretty sure that there are no further references, you could use gc.collect() to force a garbage collection run. That might help if your code doesn't do a lot of other allocations.
One thing to keep in mind is that if the big_object is created by a C extension module (like e.g. numpy), it could manage its own memory. In that case the garbage collection won't affect it! Also small integers and small strings are pre-allocated and won't be garbage collected. You can use gc.is_tracked() to check if an object is managed by the garbage collector.
What I would suggest is that you run your program with and without del+gc.collect(), and monitor the amount of RAM used. On UNIX-like systems, look at the resident set size. You could also use sys._debugmallocstats().
Unless you see the resident set size grow and grow, I wouldn't worry about it.
I know that python uses reference counting for garbage collection.
Every object that is allocated on the heap has counter that counts the number of object that refer to it, when the counter hits zero, the object is delete.
but how python handle with circle pointer?
if one of then delete the second stay with 1 counter but need to be delete.
The way this is handled is dependent on the python implementation. The reference implementation, the one you're probably using, is sometimes called CPython, because it is written in C.
CPython uses reference counting to clean up object which are obviously no longer used. However, every once in a while, it pauses execution of the program, and begins will the objects directly referenced by variables alive in the program. Then, it follows all references as long as it can, marking which objects have been visited. Once it has followed all references, it finds all the objects which aren't reachable from the main program, and deletes them. This is called tracing garbage collection, of which mark and sweep is a particular implementation.
If you want, and you're sure your program has no circular references, you can turn this feature off to improve performance. If you have circular references, however, you'll accidentally cause memory leaks, so it's usually not worth doing unless you're really worried about performance.
In Cython 0.25 the no_gc directive was added. The documentation for this new directive (as well as for a related no_gc_clear directive) can be found here, but the only thing I really understand about it is that it can speed up your code be disabling certain aspects of garbage collection.
I am interested because I have some high performance Cython code which uses extension types, and I understand that no_gc can speed things up further. In my code, instances of extension types are always left alive until the very end when the program closes, which makes me think that disabling garbage collection for these might be OK.
I guess what I really need is an example where the usage of no_gc goes bad and leads to memory leaks, together with en explanation of exactly why that happens.
It's to do with circular references - when instance a holds a reference to a Python object that references a again then a can never be freed through reference counting so Python tries to detect the cycle.
A very trial example of a class that could cause issues is:
# Cython code:
cdef class A:
cdef param
def __init__(self):
self.param = self
(and some Python code to run it)
import cython_module
while True:
cython_module.A()
This is fine as is (the cycles are detected and they get deallocated every so often) but if you add no_gc then you will run out of memory.
A more realistic example might be a parent/child pair that store a reference to each other.
It's worth adding that the performance gains likely to be small. The garbage collector is only run occasionally in situations when a lot of objects have been allocated and few have been freed (https://docs.python.org/3/library/gc.html - see set_threshold). It's hopefully unlikely that this describes your high performance code.
There's probably also a small performance cost on allocation and deallocation of your objects with GC, to add/remove them from the list of tracked objects (but again, hopefully you aren't allocating/deallocting huge numbers)
Finally, if your class never stores any references to Python objects then it's effectively no_gc anyway. Setting the option will do no harm but also do no good.
From what I've read about cpython it seems like it does reference counting + something extra to detect/free objects pointing to each other.(Correct me if I'm wrong). Could someone explain the something extra? Also does this guarantee* no cycle leaking? If not is there any research into an algorithm proven to add to reference counting to make it never leak*? Would this be just running a non reference counting tracing gc every so often?
*discounting bugs and problems with modules using foreign function interface
As explained in the documentation for gc.garbage, there is no guarantee that no leaks occur; specifically, cyclic objects with __del__ methods are not collected by default. For such objects, the cyclic links have to be manually broken to enable further GC.
From what I understand by browsing the CPython sourcecode, the interpreter keeps references to all objects under its control. The "extra" garbage collector runs a mark-and-sweep-like algorithm through the heap, remembers for each object if it is reachable from the "outside" and, if not, deletes it. (The GC is generational, but it may be run explicitly from the gc module with a generation argument.)
The only efficient algorithm that I could think of that satisfies your criteria would indeed be a "full" GC algorithm to augment reference counting and this is what seems to be implemented in Python. I'm not an expert in these matters though.
I have a Django application that exhibits some strange garbage collection behavior. There is one view in particular that will just keep growing the VM size significantly every time it is called - up to a certain limit, at which point usage drops back again. The problem is that it's taking considerable time until that point is reached, and in fact the virtual machine running my app doesn't have enough memory for all FCGI processes to take as much memory as they then sometimes do.
I've spent the last two days investigating this and learning about Python garbage collection, and I think I do understand what is happening now - for the most part. When using
gc.set_debug(gc.DEBUG_STATS)
Then for a single request, I see the following output:
>>> c = django.test.Client()
>>> c.get('/the/view/')
gc: collecting generation 0...
gc: objects in each generation: 724 5748 147341
gc: done.
gc: collecting generation 0...
gc: objects in each generation: 731 6460 147341
gc: done.
[...more of the same...]
gc: collecting generation 1...
gc: objects in each generation: 718 8577 147341
gc: done.
gc: collecting generation 0...
gc: objects in each generation: 714 0 156614
gc: done.
[...more of the same...]
gc: collecting generation 0...
gc: objects in each generation: 715 5578 156612
gc: done.
So essentially, a huge amount of objects are allocated, but are initially moved to generation 1, and when gen 1 is sweeped during the same request, they are moved to generation 2. If I do a manual gc.collect(2) afterwards, they are removed. And, as I mentioned, there also removed when the next automatic gen 2 sweep happens, which, if I understand correctly, would in this case something like every 10 requests (at this point the app needs about a 150MB).
Alright, so initially I thought that there might be some cyclic referencing going on within the processing of one request that prevents any of these objects from being collected within the handling of that request. However, I've spent hours trying to find one using pympler.muppy and objgraph, both after and by debugging inside the request processing, and there don't seem to be any. Rather, it seems the 14.000 objects or so that are created during the request are all within a reference chain to some request-global object, i.e. once the request goes away, they can be freed.
That has been my attempt at explaining it, anyway. However, if that's true and there are indeed no cycling dependencies, shouldn't the whole tree of objects be freed once whatever request object that causes them to be held goes away, without the garbage collector being involved, purely by virtue of the reference counts dropping to zero?
With that setup, here are my questions:
Does the above even make sense, or do I have to look for the problem elsewhere? Is it just an unfortunate accident that significant data is kept around for so long in this particular use case?
Is there anything I can do to avoid the issue. I already see some potential to optimize the view, but that appears to be a solution with limited scope - although I am not sure what I generic one would be, either; how advisable is it for example to call gc.collect() or gc.set_threshold() manually?
In terms of how the garbage collector itself works:
Do I understand correctly that an object is always moved to the next generation if a sweep looks at it and determines that the references it has are not cyclic, but can in fact be traced to a root object.
What happens if the gc does a, say, generation 1 sweep, and finds an object that is referenced by an object within generation 2; does it follow that relationship inside generation 2, or does it wait for a generation 2 sweep to occur before analyzing the situation?
When using gc.DEBUG_STATS, I care primarily about the "objects in each generation" info; however, I keep getting hundreds of "gc: 0.0740s elapsed.", "gc: 1258233035.9370s elapsed." messages; they are totally inconvenient - it takes considerable time for them to be printed out, and they make the interesting things a lot harder to find. Is there a way to get rid of them?
I don't suppose there is a way to do a gc.get_objects() by generation, i.e. only retrieve the objects from generation 2, for example?
Does the above even make sense, or do I have to look for the problem elsewhere? Is it just an unfortunate accident that significant data is kept around for so long in this particular use case?
Yes, it does make sense. And yes, there are other issues worth to consider. Django uses threading.local as base for DatabaseWrapper (and some contribs use it to make request object accessible from places where it's not passed explicitly). These global objects survive requests and can keep references to objects till some other view is handled in the thread.
Is there anything I can do to avoid the issue. I already see some potential to optimize the view, but that appears to be a solution with limited scope - although I am not sure what I generic one would be, either; how advisable is it for example to call gc.collect() or gc.set_threshold() manually?
General advice (probably you know it, but anyway): avoid circular references and globals (including threading.local). Try to break cycles and clear globals when django design makes hard to avoid them. gc.get_referrers(obj) might help you to find places requiring attention. Another way it to disable garbage collector and call it manually after each request, when it's the best place to do (this will prevent objects from moving to the next generation).
I don't suppose there is a way to do a gc.get_objects() by generation, i.e. only retrieve the objects from generation 2, for example?
Unfortunately this is not possible with gc interface. But there are several ways to go. You can consider the end of list returned by gc.get_objects() only, since objects in this list are sorted by generation. You can compare the list with one returned from previous call by storing weak references to them (e.g. in WeakKeyDictionary) between calls. You can rewrite gc.get_objects() in your own C module (it's easy, mostly copy-paste programming!) since they are stored by generation internally, or even access internal structures with ctypes (requires quite deep ctypes understanding).
I think your analysis looks sound. I'm not an expert on the gc, so whenever I have a problem like this I just add a call to gc.collect() in an appropriate, non time critical place, and forget about it.
I'd suggest you call gc.collect() in your view(s) and see what effect it has on your response time and your memory usage.
Note also this question which suggests that setting DEBUG=True eats memory like it is nearly past its sell by date.